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Algorithmic attention rents: A theory of digital platform market power
Abstract
We outline a theory of algorithmic attention rents in digital aggregator platforms. We explore the way that as platforms grow, they become increasingly capable of extracting rents from a variety of actors in their ecosystems—users, suppliers, and advertisers—through their algorithmic control over user attention. We focus our analysis on advertising business models, in which attention harvested from users is monetized by reselling the attention to suppliers or other advertisers, though we believe the theory has relevance to other online business models as well. We argue that regulations should mandate the disclosure of the operating metrics that platforms use to allocate user attention and shape the “free” side of their marketplace, as well as details on how that attention is monetized.
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“Online social media markets are governed by algorithmic allocations of attention (O'Reilly et al. 2024; Strauss et al. 2024), not prices.”
From "Rich-Get-Richer"? Platform Attention and Earnings Inequality using Patreon Earnings Data (2025) · cites this paper as O'Reilly et al. 2024
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“The relationship between platforms' algorithmic choices over what content to prioritize and their market power has been theorized in the context of advertising and data privacy (Srinivasan 2019; Strauss et al. 2024; O'Reilly et al. 2024).”
From "Rich-Get-Richer"? Platform Attention and Earnings Inequality using Patreon Earnings Data (2025) · cites this paper as O'Reilly et al. 2024
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Self-citationThe Market Power of Technology: Understanding the Second Gilded Age
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Cited by
Sorted by citations
"Rich-Get-Richer"? Platform Attention and Earnings Inequality using Patreon Earnings Data
Strauss, I., Yang, J., & Mazzucato, M. (2025) · arXiv
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Metadata
- Authors
- O’Reilly, T., Strauss, I., & Mazzucato, M.
- Venue
- Data & Policy
- Publisher
- Cambridge University Press (CUP)
- Published
- 2024
- Volume
- 6
- ISSN
- 2632-3249
- Cited by
- 37
- References
- 6
- Language
- en
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